Please use this identifier to cite or link to this item:
https://ptsldigital.ukm.my/jspui/handle/123456789/578542| Title: | Empirical investigation of feature sets effectiveness in product review sentiment classification |
| Authors: | Nurfadhlina Mohd Sharef (UPM) Rozilah Rosli (UPM) |
| Keywords: | Product review Sentiment classification Sentiment features |
| Issue Date: | Jun-2017 |
| Description: | Sentiment analysis classification has been typically performed by combining features that represent the dataset at hand. Existing works have employed various features individually such as the syntactical, lexical and machine learning, and some have hybridized to reach optimistic results. Since the debate on the best combination is still unresolved this paper addresses the empirical investigation of the combination of features for product review classification. Results indicate the Support Vector Machine classification model combined with any of the observed lexicon namely MPQA, BingLiu and General Inquirer and either the unigram or inte-gration of unigram and bigram features is the top performer. |
| News Source: | Pertanika Journals |
| ISSN: | 0128-7680 |
| Volume: | 25 |
| Pages: | 125-132 |
| Publisher: | Universiti Putra Malaysia Press |
| Appears in Collections: | Journal Content Pages/ Kandungan Halaman Jurnal |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_116008+Source01+Source010.PDF | 446.4 kB | Adobe PDF |  View/Open |
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